Chunk #25 — Mature Network Architecture Develops Via Segregation and Integration — Developmental Changes in Functional Relationships Observed with Support Vector Machines
support vector machine was 91% accurate. In addition to classification, SVM regression was used to predict an individual's relative brain maturity on a functional connectivity maturation index (See Fig. 4). The functional maturation curve, derived from SVM regression, accounted for 55% of the sample variance and followed a classic nonlinear growth curve shape. For both the SVM classification and regression approaches, the features used by the SVM to make its determinations were predominately those that reflected segregation of networks (decreased correlations between anatomically local regions) with age (Dosenbach et al. 2010).
